Multiple-input multiple-output (MIMO) is a technology for next generation wireless systems to enhance the capacity and robustness of the communication link. MIMO technology is based on the presence of multiple transmit antennas and multiple receive antennas in the communication link. Application of MIMO technology is envisioned for cellular communication, broadband wireless access, as well as for wireless local area networks (WLANs). A plurality of two or more transmit antennas is also referred to as an array of transmit antennas herein.
The benefits of MIMO communication are obtained through a combination of antenna arrays that provide spatial diversity from the propagation channel and algorithms that can adapt to the changing multivariate channel.
In future mobile systems and in the long-term evolution of the Universal Mobile Telecommunication System (UMTS LTE) the use of multiple-antenna techniques will become increasingly important to meet spectral efficiency requirements. A significant gain in spectral efficiency can be achieved in a downlink transmission by multiplexing multiple codewords in the spatial domain to either a single user or multiple users sharing the same time-frequency resource block. These single-user or multi-user MIMO schemes exploiting the multiplexing gain of multi-antenna transmission are sometimes referred to as spatial division multiplexing (SDM) and spatial division multiple access (SDMA) techniques. An SDMA scheme enables multiple users within the same radio cell to be accommodated on the same frequency or time slot. The realization of this technique can be accomplished by using an antenna array, which is capable of modifying its time, frequency, and spatial response by means of the amplitude and phase weighting and an internal feedback control.
Beamforming is a method used to create a radiation pattern of the antenna array by constructively adding the phases of the signals in the direction of the communication targets (terminal devices) desired, and nulling the pattern of the communication targets that are undesired or interfering.
In this context, the beamforming vector plays an important role. For purposes of illustration of the meaning of the beamforming vector, in an exemplary single-user communication system employing transmit beamforming and receive combining, assuming that signalling is done using M transmit and N receive antennas, the input-output relationship of this communication system is given byy=zHHwx+zHn where H is a N×M channel matrix connecting the transmitter and the receiver, z is the receive combining vector, zH is its Hermitian transpose, w is the transmit beamforming vector, x is the transmitted symbol from a chosen constellation, and n is independent noise added at the receiver.
One of the challenges in the design of the beamforming vectors for SDM and SDMA techniques is the need for the base station to know the channels for all the users and receiving antennas of each user. This would require a large amount of feedback to be signalled from the users to the base station.
Solutions have been proposed to reduce this signalling information by introducing a codebook of few possible beamforming matrices. Each user then applies a greedy procedure to select one or more preferred beamforming vectors out of the codebook, by evaluating the Signal-to-Noise-Ratios (SINRs) of different beamforming combinations. Thus, each user has to signal one or several indexes of the preferred vector or vectors, respectively, plus one or more Channel-Quality-Indicator (CQI) values, indicating the corresponding SINRs.
An issue with codebook-based solutions is that the beamforming vectors are not jointly optimized according to the channel conditions. The base station uses the feedback information from the users only to schedule transmission to the set of users reporting the best CQI values.
Alternatively, significant gain in the cell throughput can be achieved if the base station could implement an ad-hoc design of the beamformer. This is possible, for example, if the users report all the channel coefficients, after some quantization operation. However, this requires signalling as many complex values as the product, MN, between the number M of transmit antennas and the number N of receive antennas per user.
Similarly, a solution to this problem based on channel quantization and zero forcing beamforming is described in “Transform-Domain Feedback Signalling for MIMO Communication” in the applicant patent application having reference PH006732EP4.
Another approach is PU2RC (per-user unitary rate control)
In FIG. 1 a block diagram is drawn of the fundamental operations carried out at the transmitter (Node B) and receivers (UE's) in a typical multi-user downlink MIMO scheme for which the present invention applies. The method described here is used to reduce the number of bits required to signal the precoding matrix U from the Node B to each UE selected for transmission.
FIG. 1 is a Block diagram of the downlink multi-user MIMO operations carried out at the transmitter and receivers. The AMC block performs adapatation of modulation and coding for each apatial stram to be transmitted. The feedback from each UE consists of a PMI (precoding matrix indicator) index selected from a codebook of vectores and a real-valued CQI (channel quality indicator), which is an estimate of the SINR for the relevant received spatial stream. The precoding matrix, which may be determined based on PMI and CQI feedback from UEs, modifies the spatial response of the transmitting antenna array. This precoding matrix may vary in the time and/or frequency domain.
The feedback from the Node B conveys quantised information on the vectors forming the precoding matrix U. This feedback from the Node B is the subject of the present invention.
In order to build the optimum receiver, it is necessary to know the precoding vectors used by the base station for all simultaneously-scheduled users. This enables a receiver with multiple receive antennas to derive the phase reference for its own data as well as to compute combining coefficients for rejection of interference from the signals for other users.
However, this results in a very large downlink signalling overhead.